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Record W2171181546 · doi:10.1109/pac.1991.164444

Measurements of high-temperature RF and microwave properties of selected aluminas and ferrites used in accelerators

2002· article· en· W2171181546 on OpenAlexaff
R. M. Hutcheon, M.S. de Jong, P.G. Lucuta, Juliette E. McGregor, Bob H. Smith, F.P. Adams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMicrowaveMaterials scienceRadio frequencyDielectricCeramicPermittivityDielectric lossDielectric heatingTemperature measurementAtmospheric temperature rangeOptoelectronicsNuclear engineeringNuclear magnetic resonanceElectrical engineeringComputer scienceComposite materialEngineeringTelecommunicationsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Modern accelerator design practice includes the use of alumina RF and microwave windows and high-quality ferrites in applications with ever-increasing requirements on power handling ability. Modeling studies of such designs are of increasing economic importance, but frequently are hindered by a lack of measured values of the ceramic loss factors. AECL has developed a system to measure the complex permittivity of small samples over a frequency range from 50 to 2450 MHz and up to 1000 degrees C in temperature. Samples of RF window materials from several suppliers have been studied, with a view to relating the dielectric loss factor to the microscopic material properties. The temperature dependence of the dielectric and magnetic loss factors of a few relevant ferrites were measured at selected frequencies. The measuring methods are described, and the results are presneted.>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.196
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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